# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os
import sys
import tempfile
from glob import glob

import torch
from PIL import Image

import monai
from monai.data import create_test_image_2d, list_data_collate, decollate_batch, DataLoader
from monai.inferers import sliding_window_inference
from monai.metrics import DiceMetric
from monai.networks.nets import UNet
from monai.transforms import (
    Activations,
    EnsureChannelFirstd,
    AsDiscrete,
    Compose,
    LoadImaged,
    SaveImage,
    ScaleIntensityd,
)


def main(tempdir):
    monai.config.print_config()
    logging.basicConfig(stream=sys.stdout, level=logging.INFO)

    print(f"generating synthetic data to {tempdir} (this may take a while)")
    for i in range(5):
        im, seg = create_test_image_2d(128, 128, num_seg_classes=1)
        Image.fromarray((im * 255).astype("uint8")).save(os.path.join(tempdir, f"img{i:d}.png"))
        Image.fromarray((seg * 255).astype("uint8")).save(os.path.join(tempdir, f"seg{i:d}.png"))

    images = sorted(glob(os.path.join(tempdir, "img*.png")))
    segs = sorted(glob(os.path.join(tempdir, "seg*.png")))
    val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]

    # define transforms for image and segmentation
    val_transforms = Compose(
        [
            LoadImaged(keys=["img", "seg"]),
            EnsureChannelFirstd(keys=["img", "seg"]),
            ScaleIntensityd(keys=["img", "seg"]),
        ]
    )
    val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
    # sliding window inference need to input 1 image in every iteration
    val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate)
    dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
    post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
    saver = SaveImage(output_dir="./output", output_ext=".png", output_postfix="seg", scale=255)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = UNet(
        spatial_dims=2,
        in_channels=1,
        out_channels=1,
        channels=(16, 32, 64, 128, 256),
        strides=(2, 2, 2, 2),
        num_res_units=2,
    ).to(device)

    model.load_state_dict(torch.load("best_metric_model_segmentation2d_dict.pth", weights_only=True))

    model.eval()
    with torch.no_grad():
        for val_data in val_loader:
            val_images, val_labels = val_data["img"].to(device), val_data["seg"].to(device)
            # define sliding window size and batch size for windows inference
            roi_size = (96, 96)
            sw_batch_size = 4
            val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)
            val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
            val_labels = decollate_batch(val_labels)
            # compute metric for current iteration
            dice_metric(y_pred=val_outputs, y=val_labels)
            for val_output in val_outputs:
                saver(val_output)
        # aggregate the final mean dice result
        print("evaluation metric:", dice_metric.aggregate().item())
        # reset the status
        dice_metric.reset()


if __name__ == "__main__":
    with tempfile.TemporaryDirectory() as tempdir:
        main(tempdir)
